
Using Convolutional Neural Networks (CNN) [1]}, [2]}, [3]}, [4]} is a common approach for hand movement classification, where sEMG signals are first converted into images and then used as input for CNN-based architectures. However, the nature of sEMG signals is sequential, and CNN architectures only take into account the spatial features of the sEMG signals. Therefore, in recent literature [5]}, [6]}, [7]}, authors proposed using recurrent-based architectures such as Long Short Term Memory (LSTM) networks to exploit the temporal features of sEMG signals. On the other hand, it is suggested [8]}, [9]}, [10]} to use hybrid models (CNN-LSTM architecture) instead of using a single model to capture the temporal and spatial characteristics of sEMG signals. Although recent academic researchers are improving the performance by using Recurrent Neural Networks (RNNs) or hybrid architectures, the sequence modeling with recurrent-based architectures has several drawbacks such as consuming high memory, lack of parallelism, and lack of stable gradient during the training [11]}, [12]}. It is demonstrated [13]} that sequence modeling using RNN-based models does not always outperform CNN-based designs. Specifically, CNN architectures have several advantages over RNNs such as lower memory requirements and faster training if designed properly [13]}. Therefore, in the recent literature [11]}, [12]}, [17]}, [18]}, the authors took advantage of 1-D Convolutions developed based on the dilated causal convolutions, where the sequence of sEMG signals can be processed as a whole with lower memory requirement during the training compared to RNNs. Convolution operation in CNNs, however, has two main limitations, i.e., (i) it has a local receptive field, which makes it incapable of modeling global context, and; (ii) their learned weights remain stationary at inference time, therefore, they cannot adapt to changes in input. Attention mechanism [19]} can mitigate both of these problems. Consequently, the authors in the recent research papers [20]}, [21]}, [22]}, [23]}, [24]} used the attention mechanism combined with CNNs and/or RNNs to improve the performance of sEMG-based HGR. The attention mechanism's major disadvantage is that it is often computationally intensive. Therefore, a carefully engineered design is required to make attention-based models computationally viable, especially for low-power devices.
